Skip to main content
Practice

Gradient of the Loss Function

The gradient represents the slope of the loss function, indicating how to adjust the model's parameters to reduce the loss.


Role of the Gradient​

It shows how quickly the loss function changes at specific weights and biases. Simply put, the gradient tells us how to tweak the model's parameters to minimize the loss function.

Reference : What does it mean for AI to 'learn'?

  1. Loss Function: A function that represents the difference between the model's prediction and the actual value. Minimizing the value of this function is the goal of model training.

  2. Gradient Calculation: Calculates the slope of the loss function, representing how the function changes based on specific weights and biases. Mathematically, it is the derivative of the loss function with respect to each parameter.

  3. Parameter Update: Utilizes the gradient to update parameters (weights and biases). Typically, Gradient Descent is used to update parameters in the direction of minimizing the loss function, moving in the opposite direction of the slope.


Metaphorical Understanding​

  1. Model Prediction and Error:

    • You're trying to shoot arrows at a target. The goal is to hit close to the center of the target.

    • Here, the center of the target is the "correct answer," and where you shoot the arrow is the "model's prediction."

    • The distance from the center to the arrow's position is the "error" or "loss."

  2. Calculating the Gradient:

    • After the first arrow is shot, check how far it deviated from the target's center.

    • This deviation represents the gradient, showing how far and in which direction the arrow missed the center.

  3. Adjusting Using the Gradient:

    • When shooting the second arrow, consider how the first arrow deviated.

    • If the first arrow missed to the upper right, aim more precisely to the lower left for the next shot.

    • Use the gradient (direction and degree of the first arrow's miss) to adjust the direction of the subsequent arrow.


Difference between Loss Function Return Value and Gradient​

In the target practice game, the return value of the loss function indicates how far the arrow landed from the target center when you shot it. For instance, if the center and the arrow hit point are 10 meters apart, the loss value is 10.

The gradient value indicates the direction and degree by which the arrow missed the center. For example, if the arrow deviated 10 degrees right and 5 degrees up, it provides this information to adjust your aim for the next shot.

As learning progresses, just as you gradually hit closer to the target's center, the AI model also improves its predictions by adjusting the weights and biases using the gradient.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.